DBMS > Google BigQuery vs. HEAVY.AI vs. PouchDB vs. Splunk

System Properties Comparison Google BigQuery vs. HEAVY.AI vs. PouchDB vs. Splunk

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

JavaScript DBMS with an API inspired by CouchDB

Analytics Platform for Big Data

Primary database model

Relational DBMS

Document store

Search engine

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	89.10
Rank	#14	Overall
	#2	Search engines

Website

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

pouchdb.com

www.splunk.com

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

pouchdb.com/guides

docs.splunk.com/Documentation/Splunk

Developer

Google

HEAVY.AI, Inc.

Apache Software Foundation

Splunk Inc.

Initial release

2010

2016

2012

2003

Current release

5.10, January 2022

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial

Limited free edition and free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

JavaScript

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

Splunk Search Processing Language for search commands

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

HTTP REST

only for PouchDB Server
JavaScript API

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles

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